DSSs are currently used for medical purposes as well as consumer-facing online and mobile products. Within the context of medical use, DSSs include the following: (i) expensive, stand-alone products that contain sensory components that directly measure physiological inputs; (ii) integrated components in hospital-outfitted or network-outfitted electronic medical records (EMRs); (iii) inexpensive calculators designed for healthcare professionals (e.g., EPOCRATES®, Epocrates, Inc., San Mateo, Calif.); and (iv) free calculators that populate many healthcare-related content websites to engage viewers (e.g., WEBMD®, WebMD, LLC, New York, N.Y.).
Often, the core function of a DSS is one or more algorithms, which, when executed, provides the probability of a certain clinical or health outcome. Such probabilities are useful tools for healthcare providers in making health-related decisions relating to patient treatment and also to consumers in taking steps to implement and/or maintain a healthy lifestyle. Most patients and consumers, and even many healthcare providers, may not be aware of, or have the time to scrutinize, the quality of these decision support tools. For those healthcare providers who devote the time and expense to research and implement high quality calculators and decision tools, their improved quality of counseling and healthcare delivery may not be acknowledged and/or recognized on the clinical side by their healthcare organizations, patients, payors, and healthplan sponsors. Such shortcomings are not conducive to the widespread implementation and use of rigorously validated DSSs.
Currently, EHR platforms are designed to create and maintain health records pertaining to the following: (i) a patient's general medical health; (ii) a patient's engagement with their healthcare provider; (iii) improved physician-patient-family communications; and (iv) a reduction in the redundancy and incidence of errors in a patient's health care records. These EHR platforms, while useful, do not cater to the special needs of individuals who have health-lifestyle-medical needs that require creating and keeping records that are not supported by standard EHR platforms. Further, the style and language that currently available EHRs use to prompt input from individuals is not designed to optimize extraction of accurate and essential data from complex medical history, specialized medical needs, or medical history that is closely intertwined with lifestyle factors, such as reproductive and developmental history, peri-conceptional concerns, fertility data, and/or chronic illnesses.
EHR platforms are also not designed to verify or require certain personal health data input that is required to run prediction models. To the best of the inventor's knowledge, there are no known EHRs that are integrated with validated prediction models, transparency of data source, and/or methodology that allow the quality of the EHRs to be objectively assessed. In this regard, while an individual may be diligent in creating and maintaining their own personal health records, the individual receives no prognostic benefit from the data input exercise. Further, because currently available predication models are not validated, the individual has no foundation for verifying the accuracy of the prediction.
Within the context of reproductive care, current EHR systems are not being used to support personal health data input that is required to make predictions that address probable outcomes of specific treatments, such as for example, the prognosis of a live birth event in fertility treatments or the prognosis of the outcome of a particular disease state. In a similar vein, no currently available DSS method is being used to provide personalized prognostic information relating to patient fertility treatments.
While DSSs and EHRs are currently used for medical and consumer products as described above, to the best of the inventor's knowledge, these two systems have not been effectively used in concert to deliver tools for use by health care professionals and/or consumers.